{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOdZWtBBXigCbzReXOA7+up",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alessandropequeno/PPGEEC-Deep-Learning-UFRN/blob/main/Quest%C3%A3o_1_b.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1) Considere o problema das espirais. Sendo a espiral 1 uma classe e a espiral 2 outra classe. Gere os dados usando as seguintes equações: para espiral 1: x = Θ/4 cosΘ :. y = Θ/4 senΘ :. Θ ≥ 0\n",
        "\n",
        "para espiral 2: x = (Θ/4 + 0.8) cosΘ :. y = (Θ/4 + 0.8) senΘ :. Θ ≥ 0\n",
        "\n",
        "fazendo Θ assumir 1000 valores igualmente espaçados entre 0 e 20 radianos. Solucione este problema considerando:\n",
        "\n",
        "b) Um comitê de máquina formado por uma rede perceptron de uma camada oculta, uma\n",
        "RBF e uma máquina de vetor de suporte (SVM)"
      ],
      "metadata": {
        "id": "_ufyKBqW_rSK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Implementando as Bibliotecas"
      ],
      "metadata": {
        "id": "9VMFubxvASAH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.base import BaseEstimator, ClassifierMixin\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics.pairwise import euclidean_distances\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim"
      ],
      "metadata": {
        "id": "p6ImdFs93T4f"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### gerando os dados"
      ],
      "metadata": {
        "id": "fr7PuLzMAj6n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Gerar dados em espiral\n",
        "theta = np.linspace(0, 20, 1000)\n",
        "X_spiral1 = np.column_stack([theta / 4 * np.cos(theta), theta / 4 * np.sin(theta)])\n",
        "X_spiral2 = np.column_stack([((theta / 4) + 0.8) * np.cos(theta), ((theta / 4) + 0.8) * np.sin(theta)])\n",
        "X = np.vstack([X_spiral1, X_spiral2])\n",
        "y = np.hstack([np.zeros(len(X_spiral1)), np.ones(len(X_spiral2))])\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "pqESMxTC3Xbl"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Classe RBF"
      ],
      "metadata": {
        "id": "PlvjJNLw3j9w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class RBFNet(BaseEstimator, ClassifierMixin):\n",
        "    def __init__(self, num_centers=10, gamma=1.0):\n",
        "        self.num_centers = num_centers\n",
        "        self.gamma = gamma\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        kmeans = KMeans(n_clusters=self.num_centers, random_state=42)\n",
        "        kmeans.fit(X)\n",
        "        self.centers = kmeans.cluster_centers_\n",
        "        distances = euclidean_distances(X, self.centers)\n",
        "        self.std_dev = np.std(distances)\n",
        "        phi = np.exp(-self.gamma * (distances / self.std_dev) ** 2)\n",
        "        self.weights = np.linalg.lstsq(phi, y, rcond=None)[0]\n",
        "\n",
        "    def predict(self, X):\n",
        "        distances = euclidean_distances(X, self.centers)\n",
        "        phi = np.exp(-self.gamma * (distances / self.std_dev) ** 2)\n",
        "        return np.dot(phi, self.weights)\n",
        "\n",
        "param_grid_rbf = {\n",
        "    'num_centers': [5, 10, 15, 20],\n",
        "    'gamma': [0.1, 0.5, 1.0, 2.0, 5.0]\n",
        "}\n",
        "\n",
        "rbf_model = RBFNet()\n",
        "grid_search_rbf = GridSearchCV(rbf_model, param_grid_rbf, cv=5)\n",
        "grid_search_rbf.fit(X_train, y_train)\n",
        "\n",
        "print(\"Best parameters for RBF Network:\", grid_search_rbf.best_params_)\n",
        "best_rbf_model = grid_search_rbf.best_estimator_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EQTiGCmz3qC1",
        "outputId": "b40cca0e-87e5-4355-c5b1-ae1706f06773"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 444, in _passthrough_scorer\n",
            "    return estimator.score(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 668, in score\n",
            "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 192, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 221, in accuracy_score\n",
            "    y_type, y_true, y_pred = _check_targets(y_true, y_pred)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py\", line 95, in _check_targets\n",
            "    raise ValueError(\n",
            "ValueError: Classification metrics can't handle a mix of binary and continuous targets\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:952: UserWarning: One or more of the test scores are non-finite: [nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
            " nan nan]\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters for RBF Network: {'gamma': 0.1, 'num_centers': 5}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### SVM"
      ],
      "metadata": {
        "id": "Ckgt9Cq434Eh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Implementar a Máquina de Vetor de Suporte (SVM)\n",
        "param_grid_svm = {\n",
        "    'svc__C': [0.1, 1, 10, 100],\n",
        "    'svc__gamma': [0.001, 0.01, 0.1, 1]\n",
        "}\n",
        "\n",
        "svm_model = make_pipeline(StandardScaler(), SVC(kernel='rbf'))\n",
        "grid_search_svm = GridSearchCV(svm_model, param_grid_svm, cv=5)\n",
        "grid_search_svm.fit(X_train, y_train)\n",
        "\n",
        "print(\"Best parameters for SVM:\", grid_search_svm.best_params_)\n",
        "best_svm_model = grid_search_svm.best_estimator_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HbHm14LR4EM_",
        "outputId": "b5afb279-8103-4fc4-aa3b-855176040bf8"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters for SVM: {'svc__C': 100, 'svc__gamma': 1}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Criar a Rede Perceptron"
      ],
      "metadata": {
        "id": "tIL1L5O74Wl5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Perceptron(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Perceptron, self).__init__()\n",
        "        self.fc1 = nn.Linear(2, 20)\n",
        "        self.fc2 = nn.Linear(20, 10)\n",
        "        self.fc3 = nn.Linear(10, 1)\n",
        "        self.activation = nn.Sigmoid()\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = torch.relu(self.fc1(x))\n",
        "        x = torch.relu(self.fc2(x))\n",
        "        x = self.activation(self.fc3(x))\n",
        "        return x\n",
        "\n",
        "    def fit(self, X, y, epochs=1000, lr=0.01):\n",
        "        criterion = nn.BCELoss()\n",
        "        optimizer = optim.Adam(self.parameters(), lr=lr)\n",
        "\n",
        "        X_torch = torch.tensor(X, dtype=torch.float32)\n",
        "        y_torch = torch.tensor(y, dtype=torch.float32).unsqueeze(1)\n",
        "\n",
        "        train_losses = []\n",
        "\n",
        "        for epoch in range(epochs):\n",
        "            optimizer.zero_grad()\n",
        "            outputs = self(X_torch)\n",
        "            loss = criterion(outputs, y_torch)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            train_losses.append(loss.item())\n",
        "            if epoch % 100 == 0:\n",
        "                print(f\"Epoch {epoch}: Loss {loss.item()}\")\n",
        "\n",
        "        plt.plot(train_losses)\n",
        "        plt.title(\"Training Loss - Perceptron\")\n",
        "        plt.xlabel(\"Epoch\")\n",
        "        plt.ylabel(\"Loss\")\n",
        "        plt.show()\n",
        "\n",
        "    def predict(self, X):\n",
        "        X_torch = torch.tensor(X, dtype=torch.float32)\n",
        "        with torch.no_grad():\n",
        "            outputs = self(X_torch)\n",
        "        return outputs.numpy().flatten()\n",
        "\n",
        "perceptron_model = Perceptron()\n",
        "perceptron_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "VSO16gPc4Ocm",
        "outputId": "33dc37c4-8d71-4065-bf4c-d95f5b314e70"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0: Loss 0.691969633102417\n",
            "Epoch 100: Loss 0.6202744245529175\n",
            "Epoch 200: Loss 0.5305225849151611\n",
            "Epoch 300: Loss 0.4786244332790375\n",
            "Epoch 400: Loss 0.449624240398407\n",
            "Epoch 500: Loss 0.4296024441719055\n",
            "Epoch 600: Loss 0.4175199270248413\n",
            "Epoch 700: Loss 0.40626147389411926\n",
            "Epoch 800: Loss 0.3991236984729767\n",
            "Epoch 900: Loss 0.3919193744659424\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Avaliação dos modelos"
      ],
      "metadata": {
        "id": "RwbL-Ke24e7K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Avaliar o desempenho de cada modelo\n",
        "y_pred_rbf = best_rbf_model.predict(X_test)\n",
        "y_pred_perceptron = perceptron_model.predict(X_test)\n",
        "y_pred_svm = best_svm_model.predict(X_test)\n",
        "\n",
        "print(\"Accuracy - RBF Network:\", accuracy_score(y_test, y_pred_rbf.round().astype(int)))\n",
        "print(\"Accuracy - Perceptron:\", accuracy_score(y_test, y_pred_perceptron.round().astype(int)))\n",
        "print(\"Accuracy - SVM:\", accuracy_score(y_test, y_pred_svm.round().astype(int)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pa8U-WeL4fWG",
        "outputId": "3dfb476c-0d77-4255-cf80-653b43394277"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy - RBF Network: 0.6925\n",
            "Accuracy - Perceptron: 0.73\n",
            "Accuracy - SVM: 0.7825\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Classe Comite de Máquinas"
      ],
      "metadata": {
        "id": "9Fs9hqyJ03BJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class CommitteeClassifier(BaseEstimator, ClassifierMixin):\n",
        "    def __init__(self, models):\n",
        "        self.models = models\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        for model in self.models:\n",
        "            if hasattr(model, \"fit\"):\n",
        "                model.fit(X, y)\n",
        "\n",
        "    def predict(self, X):\n",
        "        predictions = np.zeros((X.shape[0], len(self.models)))\n",
        "        for idx, model in enumerate(self.models):\n",
        "            predictions[:, idx] = model.predict(X)\n",
        "        return np.mean(predictions, axis=1)\n",
        "\n",
        "committee_models = [best_rbf_model, perceptron_model, best_svm_model]\n",
        "committee = CommitteeClassifier(models=committee_models)\n",
        "committee.fit(X_train, y_train)\n",
        "\n",
        "y_pred_committee = committee.predict(X_test)\n",
        "print(\"Accuracy - Committee:\", accuracy_score(y_test, y_pred_committee.round().astype(int)))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "yX72sPAr03rU",
        "outputId": "f2e7d95d-a2da-4c1f-be3d-272c8647c7e0"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0: Loss 0.38308319449424744\n",
            "Epoch 100: Loss 0.37921199202537537\n",
            "Epoch 200: Loss 0.3762505054473877\n",
            "Epoch 300: Loss 0.3731810450553894\n",
            "Epoch 400: Loss 0.36884820461273193\n",
            "Epoch 500: Loss 0.36404499411582947\n",
            "Epoch 600: Loss 0.3592296540737152\n",
            "Epoch 700: Loss 0.35362547636032104\n",
            "Epoch 800: Loss 0.347208172082901\n",
            "Epoch 900: Loss 0.3394945561885834\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy - Committee: 0.8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visualização as previsões no conjunto de pontos"
      ],
      "metadata": {
        "id": "JbVFGQ8Y5vAq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotar os resultados de cada modelo\n",
        "plt.figure(figsize=(12, 8))\n",
        "\n",
        "# Plotar as previsões da RBF Network\n",
        "plt.subplot(2, 2, 1)\n",
        "plt.scatter(X_test[:, 0], X_test[:, 1], c=y_pred_rbf, cmap=plt.cm.coolwarm, s=20)\n",
        "plt.title(\"RBF Network Predictions\")\n",
        "plt.xlabel(\"X\")\n",
        "plt.ylabel(\"Y\")\n",
        "\n",
        "# Plotar as previsões do Perceptron\n",
        "plt.subplot(2, 2, 2)\n",
        "plt.scatter(X_test[:, 0], X_test[:, 1], c=y_pred_perceptron, cmap=plt.cm.coolwarm, s=20)\n",
        "plt.title(\"Perceptron Predictions\")\n",
        "plt.xlabel(\"X\")\n",
        "plt.ylabel(\"Y\")\n",
        "\n",
        "# Plotar as previsões do SVM\n",
        "plt.subplot(2, 2, 3)\n",
        "plt.scatter(X_test[:, 0], X_test[:, 1], c=y_pred_svm, cmap=plt.cm.coolwarm, s=20)\n",
        "plt.title(\"SVM Predictions\")\n",
        "plt.xlabel(\"X\")\n",
        "plt.ylabel(\"Y\")\n",
        "\n",
        "# Plotar as previsões do comitê\n",
        "plt.subplot(2, 2, 4)\n",
        "plt.scatter(X_test[:, 0], X_test[:, 1], c=y_pred_committee, cmap=plt.cm.coolwarm, s=20)\n",
        "plt.title(\"Committee Predictions\")\n",
        "plt.xlabel(\"X\")\n",
        "plt.ylabel(\"Y\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 804
        },
        "id": "iTqYfkD75uaU",
        "outputId": "cfb7a544-909c-43cf-86e2-3b8f3b718039"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Gerar a matrix de confusão"
      ],
      "metadata": {
        "id": "3wsH9-wR6E8j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calcular e plotar as matrizes de confusão\n",
        "cm_rbf = confusion_matrix(y_test, y_pred_rbf.round().astype(int))\n",
        "cm_perceptron = confusion_matrix(y_test, y_pred_perceptron.round().astype(int))\n",
        "cm_svm = confusion_matrix(y_test, y_pred_svm.round().astype(int))\n",
        "cm_committee = confusion_matrix(y_test, y_pred_committee.round().astype(int))\n",
        "\n",
        "fig, axs = plt.subplots(2, 2, figsize=(12, 12))\n",
        "\n",
        "# Função para adicionar os valores dentro das células\n",
        "def annotate_confusion_matrix(matrix, ax):\n",
        "    for i in range(matrix.shape[0]):\n",
        "        for j in range(matrix.shape[1]):\n",
        "            ax.text(j, i, format(matrix[i, j], 'd'),\n",
        "                    ha=\"center\", va=\"center\",\n",
        "                    color=\"white\" if matrix[i, j] > matrix.max() / 2 else \"black\")\n",
        "\n",
        "# Plotando cada matriz de confusão com os valores\n",
        "axs[0, 0].imshow(cm_rbf, interpolation='nearest', cmap=plt.cm.Blues)\n",
        "axs[0, 0].set_title('Confusion Matrix - RBF Network')\n",
        "axs[0, 0].set_xlabel('Predicted Label')\n",
        "axs[0, 0].set_ylabel('True Label')\n",
        "annotate_confusion_matrix(cm_rbf, axs[0, 0])\n",
        "\n",
        "axs[0, 1].imshow(cm_perceptron, interpolation='nearest', cmap=plt.cm.Blues)\n",
        "axs[0, 1].set_title('Confusion Matrix - Perceptron')\n",
        "axs[0, 1].set_xlabel('Predicted Label')\n",
        "axs[0, 1].set_ylabel('True Label')\n",
        "annotate_confusion_matrix(cm_perceptron, axs[0, 1])\n",
        "\n",
        "axs[1, 0].imshow(cm_svm, interpolation='nearest', cmap=plt.cm.Blues)\n",
        "axs[1, 0].set_title('Confusion Matrix - SVM')\n",
        "axs[1, 0].set_xlabel('Predicted Label')\n",
        "axs[1, 0].set_ylabel('True Label')\n",
        "annotate_confusion_matrix(cm_svm, axs[1, 0])\n",
        "\n",
        "axs[1, 1].imshow(cm_committee, interpolation='nearest', cmap=plt.cm.Blues)\n",
        "axs[1, 1].set_title('Confusion Matrix - Committee')\n",
        "axs[1, 1].set_xlabel('Predicted Label')\n",
        "axs[1, 1].set_ylabel('True Label')\n",
        "annotate_confusion_matrix(cm_committee, axs[1, 1])\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lqzH18MD6EjV",
        "outputId": "6f634788-df5a-4d03-c34c-6ea7187e3aec"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 4 Axes>"
            ],
            "image/png": 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BB/XKK6/IGKNPP/1Uffr0kTHG3sOcPn1aoaGhSkxMzPQe/L2X2Lt3r6Kjo/XUU09lur5ZxkdEz5w5o40bN6p///72M+FPnz6t+Ph4hYaG6tChQzp+/LjDtiNGjHA4m2nkyJEqU6ZMll8z2Rk+fLicnZ3tz7/55hulpqbqqaeecrhu1vDhw+Xu7p7p+6pixYoO1+9ycXFRu3bt8vX7CsjAR/yAa7i7u0u6+vGp3Pjzzz/l5OSk+vXrOyz38fGRp6en/vzzT4fltWvXzrSPypUrO3yW/Gbdf//9WrBggR5++GGNGzdOXbt2Vb9+/XTfffdle/HGjDoDAwMzrWvcuLG+/vrrTBdV/PtcMj6KdvbsWfv7eD09e/ZUpUqVtGLFCu3bt0+33HKL6tevryNHjmQam56ertdff11vv/22oqOjlZaWZl+XcXp2btSoUSNPF+p87bXXtGrVKu3bt08ffPCBqlWrdt1tTp065VBfxYoVr/sRguDgYL3wwgtKS0vTzz//rBdeeEFnz57NttagoCCFhITYn/fv31+JiYkaN26cHnjgAXl7e9vXVa1a1WHsjRg0aJCmTZumqVOn6p577sm0/tChQ5KuNmzZSUxMzPYji9LVIKx9+/b67rvvJF0NqDp27Kjbb79daWlp2r59u6pXr64zZ844/Afkzz//VHBwcKb9NW7c2L6+WbNm9uV16tTJtoYHH3xQZcqU0YEDB+Tj45PtOABA3tFn0Wf9XWH3WTabTeXLl1fjxo3tQdLJkyeVkJCgd999N9u7R548edLh+d97iYxrhl7bb/xdVFSUjDGaMGGCJkyYkO3r1KhRw/68QYMGDusrVqwoX1/fLI9hdv5ea3Zfjy4uLqpbt26m76uaNWtmug5b5cqV9dNPP+W6BiC3CKiAa7i7u8vPz08///xznrb7+z/a2bn2txfXMrm4+HR2r3HtD2hJKleunLZs2aJNmzZp9erVWrt2rVasWKE777xT69aty7aGvLqZuWRwdXVVv379tGTJEv3xxx+aPHlytmNfeuklTZgwQQ899JCmTZsmLy8vOTk56amnnsr1bzAlZXvmTHb27t1rb0r279+vgQMHXnebW265xeGH+6RJk3Kcm+QYIoWGhqpRo0bq3bu3Xn/99Uy/Oc5O165d9dVXX2nnzp3q1atXrrbJrYyzqIYOHapVq1ZlWp9xDF599dVsbzudm+t83H777XrxxRd16dIlfffdd3ruuefk6empZs2a6bvvvrNfV+HagCqvcvoa6Nevn5YuXarXX39d06dPv+HXAABkRp+Ve/RZ2bvZPuvvMuY3ePDgbH/R1rx5c4fneZ3nta8zduxYhYaGZjnm72FsfriRWq+VH1+LQG4RUAF/07t3b7377ruKjIxU+/btcxzr7++v9PR0HTp0yH62hnT1QosJCQn5+vGgypUrZ7qzhqRMv+WQJCcnJ3Xt2lVdu3bVzJkz9dJLL+m5557Tpk2bsvzhnFFnVncq++2331S1atUCuyXtAw88oIULF8rJyUkDBgzIdtwnn3yiLl26ZLoIeEJCgv1C21Lum9jcOH/+vMLDw9WkSRPddtttmjFjhv7xj3/Y72CTnWXLlunixYv253Xr1s3za/fq1UudOnXSSy+9pEceeSRX7/+VK1ckScnJyXl+vdwYPHiwXnjhBU2ZMsV+wfIMGRcRd3d3v+7ZWjkdo44dOyo1NVUffvihjh8/bg+i7rjjDntA1bBhQ4cLgPr7+2f7tZuxPreeeOIJ1a9fXxMnTpSHh4fGjRuX620BANdHn+WIPsuaPuta3t7eqlSpktLS0m74jPOMPujnn3/Odh8ZdZYtWzbXr3Po0CF16dLF/jw5OVkxMTHq2bOnfVlej8m1X4/XvnepqamKjo6+6bPugZvBNaiAv/nXv/6lChUq6OGHH1ZcXFym9YcPH7Zf5yfjh8Ps2bMdxsycOVOS8vUslnr16ikxMdHhdNqYmJhMd7A5c+ZMpm0zzmj5+y2ZM/j6+qply5ZasmSJQ3P2888/a926dQ4/BPNbly5dNG3aNL311ls5fqTK2dk5029qPv7440yf1c9o8LJqMvPq2Wef1dGjR7VkyRLNnDlTAQEBCgsLy/Z9zNChQweFhITYHzfaOD377LOKj4/X/PnzczX+q6++kiS1aNHihl7vejLOotq3b1+m2yG3adNG9erV02uvvZZlQHbq1Cn733M6RsHBwSpbtqxeeeUVeXl5qWnTppKuBlfbt2/Xt99+m+nsqZ49e2rnzp2KjIy0Lzt//rzeffddBQQEqEmTJnma54QJEzR27FiNHz9ec+fOzdO2AICc0Wcl2JfTZ1nbZ2VwdnbWvffeq08//TTLs/uu7WGy07p1a9WpU0ezZ8/O9N5kvK/VqlVT586d9c477ygmJiZXr/Puu+/q8uXL9udz587VlStXHO6eV6FChTwdj5CQELm4uOiNN95wOObvvfeeEhMT8/0sfCAvOIMK+Jt69erpgw8+0P3336/GjRtryJAhatasmVJTU7Vt2zZ9/PHHGjp0qKSrQUBYWJjeffddJSQkqFOnTtq5c6eWLFmie+65x+E3HjdrwIABevbZZ/WPf/xDTz75pC5cuKC5c+eqYcOGDhdunDp1qrZs2aJevXrJ399fJ0+e1Ntvv62aNWs6XET671599VX16NFD7du317Bhw+y3P/bw8LjuadM3w8nJSc8///x1x/Xu3VtTp05VeHi4brvtNu3fv1/Lli3L1JTUq1dPnp6emjdvnipVqqQKFSooODg4x+sOZWXjxo16++23NWnSJPvtmBctWqTOnTtrwoQJmjFjRp72dyN69OihZs2aaebMmRo1apTDRTK/++47Xbp0SdLVZvmLL77Qt99+qwEDBqhRo0YFVlPGtaj27dvnsNzJyUkLFixQjx491LRpU4WHh6tGjRo6fvy4Nm3aJHd3d3355ZeSroZZkvTcc89pwIABKlu2rPr06aMKFSqofPnyatOmjbZv364+ffrYfyt4xx136Pz58zp//nymgGrcuHH68MMP1aNHDz355JPy8vLSkiVLFB0drU8//TTba4Lk5NVXX1ViYqJGjRqlSpUqOVwcFABw4+iz6LOkotFnXevll1/Wpk2bFBwcrOHDh6tJkyY6c+aM9uzZo2+++SbLYPJaTk5Omjt3rvr06aOWLVsqPDxcvr6++u233/TLL7/o66+/liTNmTNHt99+u4KCgjR8+HDVrVtXcXFxioyM1LFjx/Tjjz867Dc1NVVdu3ZV//79dfDgQb399tu6/fbbHc5kb9OmjebOnasXXnhB9evXV7Vq1XTnnXdmW6u3t7fGjx+vKVOmqHv37rr77rvt+77lllvoeWAtS+4dCBQDv//+uxk+fLgJCAgwLi4uplKlSqZDhw7mzTffdLg17OXLl82UKVNMnTp1TNmyZU2tWrXM+PHjHcYYc/UWt7169cr0On+/7W52tz82xph169aZZs2aGRcXFxMYGGj+85//ZLr98YYNG0zfvn2Nn5+fcXFxMX5+fmbgwIHm999/z/Qaf79F8DfffGM6dOhgypUrZ9zd3U2fPn3Mr7/+6jAm4/X+fnvljFvZRkdHZ/ueGuN4++PsZHf74zFjxhhfX19Trlw506FDBxMZGZnlbYtXrVplmjRpYsqUKeMwz06dOpmmTZtm+ZrX7icpKcn4+/ub1q1bm8uXLzuMe/rpp42Tk5OJjIzMcQ55kd3XhjHGLF682GEOGbc3vvbh4uJiGjVqZF588UWHWxFfb985yenrMONYZ/V1sHfvXtOvXz9TpUoV4+rqavz9/U3//v3Nhg0bHMZNmzbN1KhRwzg5OWX6unnmmWeMJPPKK684bFO/fn0jyRw+fDhTTYcPHzb33Xef8fT0NG5ubqZdu3bmq6++chiT8d59/PHH2c5p165d9mVpaWlm4MCBpkyZMmblypXZv1kAgDyjz6LPKgp91rXi4uLMqFGjTK1atUzZsmWNj4+P6dq1q3n33XftY3LqJYwxZuvWreauu+4ylSpVMhUqVDDNmzc3b775psOYw4cPmyFDhhgfHx9TtmxZU6NGDdO7d2/zySef2MdkHO9vv/3WjBgxwlSuXNlUrFjRDBo0yMTHxzvsLzY21vTq1ctUqlTJSLK/z1n1Ntd66623TKNGjUzZsmVN9erVzciRI83Zs2cdxmR3TMPCwoy/v392byVww2zGcHUzAAAAAACKgsWLFys8PFy7du1S27ZtrS4HKDRcgwoAAAAAAACWIqACAAAAAACApQioAAAAAAAAYKliE1CdOXNGgwYNkru7uzw9PTVs2LAsb2V+rc6dO8tmszk8Hn30UYcxR48eVa9evVS+fHlVq1ZNzzzzjK5cuVKQUwEAACg09FAAULwMHTpUxhiuP4VSp4zVBeTWoEGDFBMTo/Xr1+vy5csKDw/XiBEj9MEHH+S43fDhwzV16lT78/Lly9v/npaWpl69esnHx0fbtm1TTEyMhgwZorJly+qll14qsLkAAAAUFnooAABQHBSLu/gdOHBATZo0cbiLwdq1a9WzZ08dO3ZMfn5+WW7XuXNntWzZUrNnz85y/X//+1/17t1bJ06cUPXq1SVJ8+bN07PPPqtTp07JxcWlQOYDAABQGOihAABAcVEszqCKjIyUp6enwymOISEhcnJy0o4dO/SPf/wj222XLVum//znP/Lx8VGfPn00YcIE+28AIyMjFRQUZG+sJCk0NFQjR47UL7/8olatWmW5z5SUFKWkpNifp6en68yZM6pSpYpsNtvNThcAABQQY4zOnTsnPz8/OTkVmysd3LCi1EPRPwEAUDwVVv9ULAKq2NhYVatWzWFZmTJl5OXlpdjY2Gy3e+CBB+Tv7y8/Pz/99NNPevbZZ3Xw4EF99tln9v1e21hJsj/Pab/Tp0/XlClTbnQ6AADAYn/99Zdq1qxpdRkFrij1UPRPAAAUbwXdP1kaUI0bN06vvPJKjmMOHDhww/sfMWKE/e9BQUHy9fVV165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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}